Developmental networks

Developmental networks

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Even more than [[signalling networks|signaling], developmental regulatory networks work in a time and space domain: time, as the number of cell divisions the fertilized cell has experienced, and space, as the context in which differentiation to a particular cell-type occurs.

 

Introductory reading

Li & Davidson (2009) Building developmental gene regulatory networks. Birth Defects Res C Embryo Today 87:123-30. (pmid: 19530131)

[ PubMed ] [ DOI ] Animal development is an elaborate process programmed by genomic regulatory instructions. Regulatory genes encode transcription factors and signal molecules, and their expression is under the control of cis-regulatory modules that define the logic of transcriptional responses to the inputs of other regulatory genes. The functional linkages among regulatory genes constitute the gene regulatory networks (GRNs) that govern cell specification and patterning in development. Constructing such networks requires identification of the regulatory genes involved and characterization of their temporal and spatial expression patterns. Interactions (activation/repression) among transcription factors or signals can be investigated by large-scale perturbation analysis, in which the function of each gene is specifically blocked. Resultant expression changes are then integrated to identify direct linkages, and to reveal the structure of the GRN. Predicted GRN linkages can be tested and verified by cis-regulatory analysis. The explanatory power of the GRN was shown in the lineage specification of sea urchin endomesoderm. Acquiring such networks is essential for a systematic and mechanistic understanding of the developmental process.

 

Contents

• Principles
• Methodologies
• Applications

 

Exercises

Longabaugh et al. (2005) Computational representation of developmental genetic regulatory networks. Dev Biol 283:1-16. (pmid: 15907831)

[ PubMed ] [ DOI ] Developmental genetic regulatory networks (GRNs) have unique architectural characteristics. They are typically large-scale, multi-layered, and organized in a nested, modular hierarchy of regulatory network kernels, function-specific building blocks, and structural gene batteries. They are also inherently multicellular and involve changing topological relationships among a growing number of cells. Reconstruction of developmental GRNs requires unique computational tools that support the above representational requirements. In addition, we argue that DNA-centered network modeling, separate descriptions of network organization and network behavior, and support for network documentation and annotation are essential requirements for computational modeling of developmental GRNs. Based on these observations, we have developed a freely available, platform-independent, open source software package (BioTapestry) which supports both the process of model construction and also model visualization, analysis, documentation, and dissemination. We provide an overview of the main features of BioTapestry. The BioTapestry software and additional documents are available from http://www.biotapestry.org. We recommend BioTapestry as the substrate for further co-development for and by the developmental biology community.

   

Further reading and resources

Principles

Yakoby et al. (2008) A combinatorial code for pattern formation in Drosophila oogenesis. Dev Cell 15:725-37. (pmid: 19000837)

[ PubMed ] [ DOI ] Two-dimensional patterning of the follicular epithelium in Drosophila oogenesis is required for the formation of three-dimensional eggshell structures. Our analysis of a large number of published gene expression patterns in the follicle cells suggests that they follow a simple combinatorial code based on six spatial building blocks and the operations of union, difference, intersection, and addition. The building blocks are related to the distribution of inductive signals, provided by the highly conserved epidermal growth factor receptor and bone morphogenetic protein signaling pathways. We demonstrate the validity of the code by testing it against a set of patterns obtained in a large-scale transcriptional profiling experiment. Using the proposed code, we distinguish 36 distinct patterns for 81 genes expressed in the follicular epithelium and characterize their joint dynamics over four stages of oogenesis. The proposed combinatorial framework allows systematic analysis of the diversity and dynamics of two-dimensional transcriptional patterns and guides future studies of gene regulation.

Materna & Davidson (2007) Logic of gene regulatory networks. Curr Opin Biotechnol 18:351-4. (pmid: 17689240)

[ PubMed ] [ DOI ] Regulatory networks of transcription factors and signaling molecules lie at the heart of development. Their architecture implements logic functions whose execution propels cells from one regulatory state to the next, thus driving development forward. As an example of a subcircuit that translates transcriptional input into developmental output, we consider a particularly simple case, the regulatory processes underlying pigment cell formation in sea urchin embryos. The regulatory events in this process can be represented as elementary logic functions.

Geard & Willadsen (2009) Dynamical approaches to modeling developmental gene regulatory networks. Birth Defects Res C Embryo Today 87:131-42. (pmid: 19530129)

[ PubMed ] [ DOI ] The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modeling provides a means of systematically untangling the complicated structure of GRNs, a framework within which to simulate the behavior of reconstructed systems and, in some cases, suites of analytic tools for exploring that behavior and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modeling of GRNs.

Montgomery et al. (2010) Annotating the regulatory genome. Methods Mol Biol 674:313-49. (pmid: 20827601)

[ PubMed ] [ DOI ] Determining the timing and molecular repertoire responsible for gene expression is fundamental to understanding a gene's function. Heritable differences in this character are increasingly regarded as explanatory for complex and common traits. For many known trait-predisposing genes, studies have sought to elucidate the associated logic behind gene regulation. However, there exist many challenges in deciphering these mechanisms. Among them, it is recognized that we have limited understanding of regulatory complexity, the current models of gene regulation have low specificity and any gene's regulatory logic is dependent on biological context. Addressing these limitations and defining the regulatory genome is an ongoing challenge for molecular biology. We discuss current efforts to define and annotate the regulatory genome by focusing on curation and text-mining activities. We further highlight the type of information and curation process for describing regulatory elements within the ORegAnno database ( www.oreganno.org ) and how the general standards for such information are changing.

Methodology

Longabaugh et al. (2009) Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. Biochim Biophys Acta 1789:363-74. (pmid: 18757046)

[ PubMed ] [ DOI ] Genetic regulatory networks (GRNs) are complex, large-scale, and spatially and temporally distributed. These characteristics impose challenging demands on software tools for building GRN models, and so there is a need for custom tools. In this paper, we report on our ongoing development of BioTapestry, an open source, freely available computational tool designed specifically for building GRN models. We also outline our future development plans, and give some examples of current applications of BioTapestry.

Istrail et al. (2010) Practical computational methods for regulatory genomics: a cisGRN-Lexicon and cisGRN-browser for gene regulatory networks. Methods Mol Biol 674:369-99. (pmid: 20827603)

[ PubMed ] [ DOI ] The CYRENE Project focuses on the study of cis-regulatory genomics and gene regulatory networks (GRN) and has three components: a cisGRN-Lexicon, a cisGRN-Browser, and the Virtual Sea Urchin software system. The project has been done in collaboration with Eric Davidson and is deeply inspired by his experimental work in genomic regulatory systems and gene regulatory networks. The current CYRENE cisGRN-Lexicon contains the regulatory architecture of 200 transcription factors encoding genes and 100 other regulatory genes in eight species: human, mouse, fruit fly, sea urchin, nematode, rat, chicken, and zebrafish, with higher priority on the first five species. The only regulatory genes included in the cisGRN-Lexicon (CYRENE genes) are those whose regulatory architecture is validated by what we call the Davidson Criterion: they contain functionally authenticated sites by site-specific mutagenesis, conducted in vivo, and followed by gene transfer and functional test. This is recognized as the most stringent experimental validation criterion to date for such a genomic regulatory architecture. The CYRENE cisGRN-Browser is a full genome browser tailored for cis-regulatory annotation and investigation. It began as a branch of the Celera Genome Browser (available as open source at http://sourceforge.net/projects/celeragb /) and has been transformed to a genome browser fully devoted to regulatory genomics. Its access paradigm for genomic data is zoom-to-the-DNA-base in real time. A more recent component of the CYRENE project is the Virtual Sea Urchin system (VSU), an interactive visualization tool that provides a four-dimensional (spatial and temporal) map of the gene regulatory networks of the sea urchin embryo.

Applications

Su (2009) Gene regulatory networks for ectoderm specification in sea urchin embryos. Biochim Biophys Acta 1789:261-7. (pmid: 19429544)

[ PubMed ] [ DOI ] The current gene regulatory network (GRN) of the sea urchin Strongylocentrotus purpuratus embryo describes the specification of the endomesodermal territories. However, the specification of the adjacent ectodermal territories of the embryo has been far less explored. Several recent studies on the cis-regulatory analysis of nodal and the early oral ectoderm determinants have provided clues on how the specification of this territory is initiated. Recently, a large-scale of gene regulatory network analysis was carried out in an effort to build the ectoderm specification GRN. The deduced ectodermal GRN model provides the first peek at the overall picture of ectoderm specification in the sea urchin embryo. This review integrates the current knowledge on the specification of the ectoderm by linking recent discoveries to the GRN model to understand the process of ectoderm specification in sea urchin embryos.

Su et al. (2009) A perturbation model of the gene regulatory network for oral and aboral ectoderm specification in the sea urchin embryo. Dev Biol 329:410-21. (pmid: 19268450)

[ PubMed ] [ DOI ] The current gene regulatory network (GRN) for the sea urchin embryo pertains to pregastrular specification functions in the endomesodermal territories. Here we extend gene regulatory network analysis to the adjacent oral and aboral ectoderm territories over the same period. A large fraction of the regulatory genes predicted by the sea urchin genome project and shown in ancillary studies to be expressed in either oral or aboral ectoderm by 24 h are included, though universally expressed and pan-ectodermal regulatory genes are in general not. The loci of expression of these genes have been determined by whole mount in situ hybridization. We have carried out a global perturbation analysis in which expression of each gene was interrupted by introduction of morpholino antisense oligonucleotide, and the effects on all other genes were measured quantitatively, both by QPCR and by a new instrumental technology (NanoString Technologies nCounter Analysis System). At its current stage the network model, built in BioTapestry, includes 22 genes encoding transcription factors, 4 genes encoding known signaling ligands, and 3 genes that are yet unknown but are predicted to perform specific roles. Evidence emerged from the analysis pointing to distinctive subcircuit features observed earlier in other parts of the GRN, including a double negative transcriptional regulatory gate, and dynamic state lockdowns by feedback interactions. While much of the regulatory apparatus is downstream of Nodal signaling, as expected from previous observations, there are also cohorts of independently activated oral and aboral ectoderm regulatory genes, and we predict yet unidentified signaling interactions between oral and aboral territories.

Wilczynski & Furlong (2010) Challenges for modeling global gene regulatory networks during development: insights from Drosophila. Dev Biol 340:161-9. (pmid: 19874814)

[ PubMed ] [ DOI ] Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans.